TY - THES A1 - Sieg, Tobias T1 - Reliability of flood damage estimations across spatial scales T1 - Verlässlichkeit von Hochwasserschadensschätzungen über räumliche Skalen N2 - Extreme Naturereignisse sind ein integraler Bestandteil der Natur der Erde. Sie werden erst dann zu Gefahren für die Gesellschaft, wenn sie diesen Ereignissen ausgesetzt ist. Dann allerdings können Naturgefahren verheerende Folgen für die Gesellschaft haben. Besonders hydro-meteorologische Gefahren wie zum Beispiel Flusshochwasser, Starkregenereignisse, Winterstürme, Orkane oder Tornados haben ein hohes Schadenspotential und treten rund um den Globus auf. Einhergehend mit einer immer wärmer werdenden Welt, werden auch Extremwetterereignisse, welche potentiell Naturgefahren auslösen können, immer wahrscheinlicher. Allerdings trägt nicht nur eine sich verändernde Umwelt zur Erhöhung des Risikos von Naturgefahren bei, sondern auch eine sich verändernde Gesellschaft. Daher ist ein angemessenes Risikomanagement erforderlich um die Gesellschaft auf jeder räumlichen Ebene an diese Veränderungen anzupassen. Ein essentieller Bestandteil dieses Managements ist die Abschätzung der ökonomischen Auswirkungen der Naturgefahren. Bisher allerdings fehlen verlässliche Methoden um die Auswirkungen von hydro-meteorologischen Gefahren abzuschätzen. Ein Hauptbestandteil dieser Arbeit ist daher die Entwicklung und Anwendung einer neuen Methode, welche die Verlässlichkeit der Schadensschätzung verbessert. Die Methode wurde beispielhaft zur Schätzung der ökonomischen Auswirkungen eines Flusshochwassers auf einzelne Unternehmen bis hin zu den Auswirkungen auf das gesamte Wirtschaftssystem Deutschlands erfolgreich angewendet. Bestehende Methoden geben meist wenig Information über die Verlässlichkeit ihrer Schätzungen. Da diese Informationen Entscheidungen zur Anpassung an das Risiko erleichtern, wird die Verlässlichkeit der Schadensschätzungen mit der neuen Methode dargestellt. Die Verlässlichkeit bezieht sich dabei nicht nur auf die Schadensschätzung selber, sondern auch auf die Annahmen, die über betroffene Gebäude gemacht werden. Nach diesem Prinzip kann auch die Verlässlichkeit von Annahmen über die Zukunft dargestellt werden, dies ist ein wesentlicher Aspekt für Prognosen. Die Darstellung der Verlässlichkeit und die erfolgreiche Anwendung zeigt das Potential der Methode zur Verwendung von Analysen für gegenwärtige und zukünftige hydro-meteorologische Gefahren. N2 - Natural extreme events are an integral part of nature on planet earth. Usually these events are only considered hazardous to humans, in case they are exposed. In this case, however, natural hazards can have devastating impacts on human societies. Especially hydro-meteorological hazards have a high damage potential in form of e.g. riverine and pluvial floods, winter storms, hurricanes and tornadoes, which can occur all over the globe. Along with an increasingly warm climate also an increase in extreme weather which potentially triggers natural hazards can be expected. Yet, not only changing natural systems, but also changing societal systems contribute to an increasing risk associated with these hazards. These can comprise increasing exposure and possibly also increasing vulnerability to the impacts of natural events. Thus, appropriate risk management is required to adapt all parts of society to existing and upcoming risks at various spatial scales. One essential part of risk management is the risk assessment including the estimation of the economic impacts. However, reliable methods for the estimation of economic impacts due to hydro-meteorological hazards are still missing. Therefore, this thesis deals with the question of how the reliability of hazard damage estimates can be improved, represented and propagated across all spatial scales. This question is investigated using the specific example of economic impacts to companies as a result of riverine floods in Germany. Flood damage models aim to describe the damage processes during a given flood event. In other words they describe the vulnerability of a specific object to a flood. The models can be based on empirical data sets collected after flood events. In this thesis tree-based models trained with survey data are used for the estimation of direct economic flood impacts on the objects. It is found that these machine learning models, in conjunction with increasing sizes of data sets used to derive the models, outperform state-of-the-art damage models. However, despite the performance improvements induced by using multiple variables and more data points, large prediction errors remain at the object level. The occurrence of the high errors was explained by a further investigation using distributions derived from tree-based models. The investigation showed that direct economic impacts to individual objects cannot be modeled by a normal distribution. Yet, most state-of-the-art approaches assume a normal distribution and take mean values as point estimators. Subsequently, the predictions are unlikely values within the distributions resulting in high errors. At larger spatial scales more objects are considered for the damage estimation. This leads to a better fit of the damage estimates to a normal distribution. Consequently, also the performance of the point estimators get better, although large errors can still occur due to the variance of the normal distribution. It is recommended to use distributions instead of point estimates in order to represent the reliability of damage estimates. In addition current approaches also mostly ignore the uncertainty associated with the characteristics of the hazard and the exposed objects. For a given flood event e.g. the estimation of the water level at a certain building is prone to uncertainties. Current approaches define exposed objects mostly by the use of land use data sets. These data sets often show inconsistencies, which introduce additional uncertainties. Furthermore, state-of-the-art approaches also imply problems of missing consistency when predicting the damage at different spatial scales. This is due to the use of different types of exposure data sets for model derivation and application. In order to face these issues a novel object-based method was developed in this thesis. The method enables a seamless estimation of hydro-meteorological hazard damage across spatial scales including uncertainty quantification. The application and validation of the method resulted in plausible estimations at all spatial scales without overestimating the uncertainty. Mainly newly available data sets containing individual buildings make the application of the method possible as they allow for the identification of flood affected objects by overlaying the data sets with water masks. However, the identification of affected objects with two different water masks revealed huge differences in the number of identified objects. Thus, more effort is needed for their identification, since the number of objects affected determines the order of magnitude of the economic flood impacts to a large extent. In general the method represents the uncertainties associated with the three components of risk namely hazard, exposure and vulnerability, in form of probability distributions. The object-based approach enables a consistent propagation of these uncertainties in space. Aside from the propagation of damage estimates and their uncertainties across spatial scales, a propagation between models estimating direct and indirect economic impacts was demonstrated. This enables the inclusion of uncertainties associated with the direct economic impacts within the estimation of the indirect economic impacts. Consequently, the modeling procedure facilitates the representation of the reliability of estimated total economic impacts. The representation of the estimates' reliability prevents reasoning based on a false certainty, which might be attributed to point estimates. Therefore, the developed approach facilitates a meaningful flood risk management and adaptation planning. The successful post-event application and the representation of the uncertainties qualifies the method also for the use for future risk assessments. Thus, the developed method enables the representation of the assumptions made for the future risk assessments, which is crucial information for future risk management. This is an important step forward, since the representation of reliability associated with all components of risk is currently lacking in all state-of-the-art methods assessing future risk. In conclusion, the use of object-based methods giving results in the form of distributions instead of point estimations is recommended. The improvement of the model performance by the means of multi-variable models and additional data points is possible, but small. Uncertainties associated with all components of damage estimation should be included and represented within the results. Furthermore, the findings of the thesis suggest that, at larger scales, the influence of the uncertainty associated with the vulnerability is smaller than those associated with the hazard and exposure. This leads to the conclusion that for an increased reliability of flood damage estimations and risk assessments, the improvement and active inclusion of hazard and exposure, including their uncertainties, is needed in addition to the improvements of the models describing the vulnerability of the objects. KW - hydro-meteorological risk KW - damage modeling KW - uncertainty KW - probabilistic approach KW - economic impacts KW - OpenStreetMap KW - hydro-meteorologische Risiken KW - Schadensmodellierung KW - Unsicherheiten KW - probabilistischer Ansatz KW - ökonomische Auswirkungen KW - OpenStreetMap Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426161 ER - TY - THES A1 - Kellermann, Patric T1 - Assessing natural risks for railway infrastructure and transportation in Austria T1 - Bewertung von Naturrisiken für Eisenbahninfrastruktur und -betrieb in Österreich N2 - Natural hazards can have serious societal and economic impacts. Worldwide, around one third of economic losses due to natural hazards are attributable to floods. The majority of natural hazards are triggered by weather-related extremes such as heavy precipitation, rapid snow melt, or extreme temperatures. Some of them, and in particular floods, are expected to further increase in terms of frequency and/or intensity in the coming decades due to the impacts of climate change. In this context, the European Alps areas are constantly disclosed as being particularly sensitive. In order to enhance the resilience of societies to natural hazards, risk assessments are substantial as they can deliver comprehensive risk information to be used as a basis for effective and sustainable decision-making in natural hazards management. So far, current assessment approaches mostly focus on single societal or economic sectors – e.g. flood damage models largely concentrate on private-sector housing – and other important sectors, such as the transport infrastructure sector, are widely neglected. However, transport infrastructure considerably contributes to economic and societal welfare, e.g. by ensuring mobility of people and goods. In Austria, for example, the national railway network is essential for the European transit of passengers and freights as well as for the development of the complex Alpine topography. Moreover, a number of recent experiences show that railway infrastructure and transportation is highly vulnerable to natural hazards. As a consequence, the Austrian Federal Railways had to cope with economic losses on the scale of several million euros as a result of flooding and other alpine hazards. The motivation of this thesis is to contribute to filling the gap of knowledge about damage to railway infrastructure caused by natural hazards by providing new risk information for actors and stakeholders involved in the risk management of railway transportation. Hence, in order to support the decision-making towards a more effective and sustainable risk management, the following two shortcomings in natural risks research are approached: i) the lack of dedicated models to estimate flood damage to railway infrastructure, and ii) the scarcity of insights into possible climate change impacts on the frequency of extreme weather events with focus on future implications for railway transportation in Austria. With regard to flood impacts to railway infrastructure, the empirically derived damage model Railway Infrastructure Loss (RAIL) proved expedient to reliably estimate both structural flood damage at exposed track sections of the Northern Railway and resulting repair cost. The results show that the RAIL model is capable of identifying flood risk hot spots along the railway network and, thus, facilitates the targeted planning and implementation of (technical) risk reduction measures. However, the findings of this study also show that the development and validation of flood damage models for railway infrastructure is generally constrained by the continuing lack of detailed event and damage data. In order to provide flood risk information on the large scale to support strategic flood risk management, the RAIL model was applied for the Austrian Mur River catchment using three different hydraulic scenarios as input as well as considering an increased risk aversion of the railway operator. Results indicate that the model is able to deliver comprehensive risk information also on the catchment level. It is furthermore demonstrated that the aspect of risk aversion can have marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies. Looking at the results of the investigation on future frequencies of extreme weather events jeopardizing railway infrastructure and transportation in Austria, it appears that an increase in intense rainfall events and heat waves has to be expected, whereas heavy snowfall and cold days are likely to decrease. Furthermore, results indicate that frequencies of extremes are rather sensitive to changes of the underlying thresholds. It thus emphasizes the importance to carefully define, validate, and — if needed — to adapt the thresholds that are used to detect and forecast meteorological extremes. For this, continuous and standardized documentation of damaging events and near-misses is a prerequisite. Overall, the findings of the research presented in this thesis agree on the necessity to improve event and damage documentation procedures in order to enable the acquisition of comprehensive and reliable risk information via risk assessments and, thus, support strategic natural hazards management of railway infrastructure and transportation. N2 - Naturgefahren haben zum Teil gravierende Auswirkungen auf die Gesellschaft und die Wirtschaft der betroffenen Region. Weltweit sind etwa ein Drittel der finanziellen Verluste durch Naturereignisse auf Hochwasser zurückzuführen. Die Schäden an Eisenbahninfrastruktur haben dabei oft großen Anteil am Gesamtschaden. Hochwasser und andere Naturgefahren werden häufig durch Extremwetterereignisse, wie etwa Starkniederschläge oder Extremtemperaturen, ausgelöst. Im Zuge des Klimawandels rechnet man für die kommenden Jahrzehnte mit einer Zunahme in der Anzahl bzw. der Schwere einiger Naturereignisse. Mit dem Ziel, die gesellschaftliche Widerstandsfähigkeit gegenüber Naturereignissen zu erhöhen, setzt man zur Gewinnung von Risikoinformationen auf sogenannte Naturrisikoanalysen. Die gegenwärtige Praxis konzentriert sich dabei meist auf die Analyse für einzelne Wirtschaftssektoren, wie etwa den Immobiliensektor. Der Transportsektor und insbesondere die Eisenbahninfrastruktur werden trotz der tragenden Rollen für die Wirtschaftskraft einer Gesellschaft jedoch weitgehend vernachlässigt. Zahlreiche Naturereignisse der letzten Jahrzehnte zeigten allerdings, dass Eisenbahninfrastruktur generell sehr schadensanfällig gegenüber Naturgefahren ist. Ziel dieser Doktorarbeit ist es, das Management von Naturgefahren für den Schienensektor in Österreich durch die Bereitstellung von neuen Risikoinformationen zu unterstützen. Hierzu wurde zum einen ein neuartiges Schadenmodell zur Schätzung von Hochwasserschäden an Eisenbahninfrastruktur entwickelt. Zum anderen wurde unter Verwendung von regionalen Klimamodellen die klimawandelbedingte Änderung der Häufigkeiten von Extremwetter¬ereignissen in Österreich untersucht und mögliche Auswirkungen auf den Eisenbahnbetrieb abgeleitet. Die Forschungsergebnisse zeigen, dass das entwickelte Hochwasserschadenmodell „RAIL“ in der Lage ist, potenzielle Schadenschwerpunkte entlang von Bahnlinien für großräumige Eisenbahnnetze zu identifizieren und damit einen wertvollen Beitrag für die gezielte Planung und Errichtung von technischen Hochwasserschutzmaßnahmen leisten kann. Ferner liefert die Untersuchung der Häufigkeitsentwicklung von Extremwetterereignissen bis zum Jahr 2040 wichtige Einblicke in die zukünftigen Herausforderungen für den Bahnbetrieb im Kontext des Klimawandels. Um aus zukünftigen Naturereignissen lernen zu können und somit ein tieferes Verständnis von Naturgefahrenprozessen und deren Auswirkungen auf Eisenbahninfrastruktur und –betrieb zu erlangen, wird die (Weiter-)Entwicklung und Anwendung von standardisierten Ereignis- und Schadendokumentationsverfahren empfohlen. KW - natural risks KW - damage modeling KW - railway infrastructure KW - Naturrisiken KW - Schadensmodellierung KW - Eisenbahninfrastruktur Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-103877 ER -